The Data Exchange with Ben Lorica
A podcast by Ben Lorica - Thursdays

Categories:
281 Episodes
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Creating Master Data at Scale with AI
Published: 4/02/2021 -
Bringing AI and computing closer to data sources
Published: 28/01/2021 -
Deep Learning in the Sciences
Published: 21/01/2021 -
Taking business intelligence and analyst tools to the next level
Published: 14/01/2021 -
Data exchanges and their applications in healthcare and the life sciences
Published: 7/01/2021 -
Key AI and Data Trends for 2021
Published: 31/12/2020 -
A Unified Management Model for Successful Data-Focused Teams
Published: 24/12/2020 -
Security and privacy for the disoriented
Published: 17/12/2020 -
The State of Responsible AI
Published: 10/12/2020 -
Improving the robustness of natural language applications
Published: 3/12/2020 -
End-to-end deep learning models for speech applications
Published: 26/11/2020 -
Securing machine learning applications
Published: 19/11/2020 -
Testing Natural Language Models
Published: 12/11/2020 -
Detecting Fake News
Published: 5/11/2020 -
The Computational Limits of Deep Learning
Published: 29/10/2020 -
Making deep learning accessible
Published: 22/10/2020 -
Building and deploying knowledge graphs
Published: 15/10/2020 -
Financial Time Series Forecasting with Deep Learning
Published: 8/10/2020 -
A programming language for scientific machine learning and differentiable programming
Published: 1/10/2020 -
Using machine learning to modernize medical triage and monitoring systems
Published: 24/09/2020
A series of informal conversations with thought leaders, researchers, practitioners, and writers on a wide range of topics in technology, science, and of course big data, data science, artificial intelligence, and related applications. Anchored by Ben Lorica (@BigData), the Data Exchange also features a roundup of the most important stories from the worlds of data, machine learning and AI. Detailed show notes for each episode can be found on https://thedataexchange.media/ The Data Exchange podcast is a production of Gradient Flow [https://gradientflow.com/].